Recovery from myocardial infarction (MI) is associated with a host of symptoms, including angina, depression, and worse quality of life (QoL) but little attention has been paid to these patient-centered health status outcomes. More than 10 million people in the US suffer from angina and approximately 500,000 new cases occur each year at an estimated cost of $20 billion dollars annually. We propose to identify genomic variants that contribute to inter-individual variation in post-MI angina and health status outcomes by using the TRIUMPH population, an NIH-funded cohort with exquisite disease-specific health status assessments at admission, and 1-month, 6-months and 1-year post-MI, along with adjudicated 1-year major adverse cardiovascular events and 5-year mortality. The study group is particularly well-qualified to perform this research, having expertise in genomics, pharmacogenomics, patient screening and risk profiling, outcomes research, and statistical genomics. We will also take advantage of the strengths of Washington University's CTSA, including its Cores and programs. Collectively, we will address the following Aims: AIM 1. To define the genetic contribution to the observed inter-individual variation in post-MI angina. The primary outcome will be post-MI angina over the first year, as measured by the well-validated, disease-specific Seattle Angina Questionnaire (SAQ) Angina Frequency score. Secondary outcomes are SAQ QoL score and depressive symptoms, as measured by PHQ-9. An unbiased GWAS approach will identify common genetic variants associated with these outcomes using two novel statistical genomic methods (Growth Curve Estimation and Pleiotropy). We will then use a novel, cost-efficient (multi-plexed, 'bar-coded') exomic sequencing method to finely map all exons in the genes under the association peaks and identify rare variants that are associated with these outcomes. AIM 2. To identify non-genomic factors that may potentially moderate the effects of the genetic variants identified in AIM 1. We will construct multivariable models that include genetic, clinical and treatment characteristics, with a specific focus upon interactions. These models, especially if important interactions with treatment are discovered, can serve as the foundation for estimating symptom outcomes as a function of treatment and, using these models, we can generate personalized treatment strategies. AIM 3. To test the feasibility of translating - into 'real world practice' - a prognostic modeling tool (PRISMTM) that includes genetic variants identified by AIM 1, to predict health status response to post-MI treatment. Our team has developed information technology with which to implement multivariable prediction models, executed with patient-specific data, in the process of clinical care. We will use these models to create a personalized risk profile and therapeutic strategy for post-MI treatment. In summary, we will use cutting edge experimental, statistical, and diagnostic methods to identify variants associated with post-MI angina and other health status outcomes and lay the foundation to personalize post-MI care and reduce symptom burden.